Rapid advancements in deep learning have led to many recent breakthroughs. While deep learning models achieve superior performance, often statistically better than humans, their adaption into safety-critical settings, such as healthcare or self-driving cars is hindered by their inability to provide safety guarantees or to analyze the inner workings of the model. We present MoET, a novel model based on Mixture of Experts, consisting of decision tree experts and a generalized linear model gating function. While decision boundaries of decision trees (used in an existing verifiable approach), are axis-perpendicular hyperplanes, MoET supports hyperplanes of arbitrary orientation as the boundaries. To support non-differentiable decision trees as experts we formulate a novel training procedure. In addition, we introduce a hard thresholding version, MoET_h, in which predictions are made solely by a single expert chosen via the gating function. Thanks to that property, MoET_h allows each prediction to be easily decomposed into a set of logical rules. Such rules can be translated into a manageable SMT formula providing rich means for verification. While MoET is a general use model, we illustrate its power in the reinforcement learning setting. By training MoET models using an imitation learning procedure on deep RL agents we outperform the previous state-of-the-art technique based on decision trees while preserving the verifiability of the models.
翻译:深层次学习的快速进步导致了许多最近的突破。虽然深层次学习模式取得了优异的性能,在统计上往往比人类好,但是,由于无法提供安全保障或分析模型的内部运行情况,因此难以适应安全临界环境,如医疗或自行驾驶汽车等。我们介绍了基于专家混合体的新颖模式,即决策树专家和普遍的线性模型定位功能。决定树的决策界限(在现有的可核查办法中使用)是轴垂直的超高平板,但是,教育和教育部支持任意取向的超高平板作为边界。为了支持无差别的决策树,作为专家,我们制定了新的培训程序。此外,我们引入了一个硬门槛版本,即MOET_h,其中的预测完全由一位通过定位功能挑选的专家来作出。由于这种属性,MOET_h允许每一项预测容易地分解成一套逻辑规则。这些规则可以转化为可操作的SMT公式,提供丰富的核查手段。虽然MoET是一种通用模型,但作为我们用来使用一种不区分性的决定树的模型,我们用一种深层次的模型来说明它的力量。我们用一种在强化的模型上学习以稳定性研究方式的模型,我们以试验为基础的模型,我们用一种强化的模型来学习。